by jsendak | Apr 17, 2024 | Computer Science
arXiv:2404.10528v1 Announce Type: new
Abstract: Active travel is an essential component in intelligent transportation systems. Cycling, as a form of active travel, shares the road space with motorised traffic which often affects the cyclists’ safety and comfort and therefore peoples’ propensity to uptake cycling instead of driving. This paper presents a unique dataset, collected by cyclists across London, that includes video footage, accelerometer, GPS, and gyroscope data. The dataset is then labelled by an independent group of London cyclists to rank the safety level of each frame and to identify objects in the cyclist’s field of vision that might affect their experience. Furthermore, in this dataset, the quality of the road is measured by the international roughness index of the surface, which indicates the comfort of cycling on the road. The dataset will be made available for open access in the hope of motivating more research in this area to underpin the requirements for cyclists’ safety and comfort and encourage more people to replace vehicle travel with cycling.
Analyzing the Importance of Active Travel Data in Intelligent Transportation Systems
Active travel, which involves modes of transportation such as cycling and walking, plays a critical role in intelligent transportation systems. In order to promote active travel and encourage more individuals to choose sustainable modes of transportation over driving, it is crucial to understand the factors that impact the safety and comfort of cyclists. A recent study has provided a unique dataset that offers valuable insights into these factors, with the potential to revolutionize research in this field.
The dataset, collected by cyclists across London, includes a wide range of data such as video footage, accelerometer, GPS, and gyroscope data. By combining these different types of data, researchers are able to gain a comprehensive understanding of the cyclist’s experience on the road.
One of the key aspects of this dataset is the inclusion of safety ranking for each frame by an independent group of London cyclists. This allows researchers to assess the safety level of specific situations, identify potential hazards, and develop strategies to mitigate risks. By understanding the objects in the cyclist’s field of vision that might affect their experience, policymakers and urban planners can take proactive measures to improve cycling infrastructure and ensure the safety of cyclists.
In addition to safety rankings, the dataset also measures the quality of the road using the international roughness index of the surface. This information provides insights into the comfort of cycling on different roads, which is another crucial factor in people’s propensity to choose cycling over driving. With this data, researchers can identify areas where road conditions need improvement and suggest interventions to enhance the overall cycling experience.
The multi-disciplinary nature of this dataset is noteworthy. It combines video footage, sensor data, and human perception to offer a holistic view of the cyclist’s environment. This interdisciplinary approach brings together elements of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, as it leverages various technologies to create an immersive understanding of active travel.
Furthermore, the dataset’s availability as an open-access resource is paramount in encouraging further research in this area. By providing access to this data, more researchers can analyze and build upon the findings, ultimately contributing to a better understanding of the requirements for cyclists’ safety and comfort. This, in turn, can help inform policy decisions and interventions aimed at promoting active travel.
In conclusion,
this unique dataset has immense potential in advancing our understanding of active travel in intelligent transportation systems. By combining various data sources and incorporating safety rankings and road quality measurements, researchers can gain valuable insights into the factors that influence cycling behavior. The interdisciplinary nature of this dataset makes it relevant to the wider field of multimedia information systems, as it integrates technologies from different domains to create a comprehensive understanding of active travel. With the dataset being made openly available, it is expected to inspire further research and drive innovation in the design of safer and more comfortable cycling infrastructure.
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by jsendak | Apr 17, 2024 | Computer Science
Expert Commentary: Optimizing Patient Scheduling in Radiation Therapy through Biomimicry Principles
In the field of medical science, continuous efforts are being made to improve treatment efficacy and patient outcomes. This study explores the integration of biomimicry principles into Radiation Therapy (RT) to optimize patient scheduling and enhance treatment results.
RT is a crucial technique in the fight against cancer, as it helps eliminate cancer cells and reduce tumor sizes. However, the process of manually scheduling patients for RT is complex and time-consuming. Automating this process through optimization methodologies has the potential to simplify scheduling and improve overall treatment outcomes.
This research utilizes three bio-inspired algorithms – Genetic Algorithm (GA), Firefly Optimization (FFO), and Wolf Optimization (WO) – to address the challenges of online stochastic scheduling in RT. By evaluating convergence time, runtime, and objective values, the comparative performance of these algorithms can be assessed.
The results of this study reveal the effectiveness of bio-inspired algorithms in optimizing patient scheduling for RT. Among the algorithms examined, Wolf Optimization (WO) consistently demonstrates superior outcomes across various evaluation criteria. The application of WO in patient scheduling has the potential to streamline processes, reduce manual intervention, and ultimately improve treatment outcomes for patients undergoing RT.
The integration of biomimicry principles and optimization methodologies in RT scheduling represents an exciting development in the field. By drawing inspiration from nature and applying evolutionary algorithms, healthcare providers can enhance the efficiency and effectiveness of patient scheduling, ultimately benefiting cancer patients and healthcare systems as a whole.
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by jsendak | Apr 16, 2024 | Computer Science
arXiv:2404.09029v1 Announce Type: new
Abstract: Over the past two decades, the surge in video streaming applications has been fueled by the increasing accessibility of the internet and the growing demand for network video. As users with varying internet speeds and devices seek high-quality video, transcoding becomes essential for service providers. In this paper, we introduce a parametric rate-distortion (R-D) transcoding model. Our model excels at predicting transcoding distortion at various rates without the need for encoding the video. This model serves as a versatile tool that can be used to achieve visual quality improvement (in terms of PSNR) via trans-sizing. Moreover, we use our model to identify visually lossless and near-zero-slope bitrate ranges for an ingest video. Having this information allows us to adjust the transcoding target bitrate while introducing visually negligible quality degradations. By utilizing our model in this manner, quality improvements up to 2 dB and bitrate savings of up to 46% of the original target bitrate are possible. Experimental results demonstrate the efficacy of our model in video transcoding rate distortion prediction.
Parametric Rate-Distortion Transcoding Model for Video Streaming
In the realm of multimedia information systems, video streaming has become a prominent application due to the widespread accessibility of the internet and the growing demand for network video. As service providers strive to cater to users with varying internet speeds and devices, transcoding, which involves converting video formats, becomes crucial.
This paper introduces a parametric rate-distortion (R-D) transcoding model, which offers a novel approach to predicting transcoding distortion at different rates without the need for video encoding. This model serves as a versatile tool for achieving visual quality improvement through trans-sizing. By understanding the trade-off between rate and distortion, service providers can optimize the transcoding process and enhance video quality in terms of peak signal-to-noise ratio (PSNR).
Multi-Disciplinary Nature
This research presents a multi-disciplinary approach by bridging concepts from multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The video transcoding model can be applied to various domains, such as virtual reality simulations, where high-quality video content is necessary for an immersive experience. By utilizing the parametric R-D transcoding model, developers can ensure that the video content meets the desired quality standards, enhancing the overall user experience.
Impact on Multimedia Information Systems
The parametric R-D transcoding model proposed in this paper contributes to the field of multimedia information systems by providing a method to optimize video quality without the need for encoding. This approach reduces computational complexity and time required for video transcoding, enabling service providers to deliver high-quality video streaming efficiently. The model’s ability to identify visually lossless and near-zero-slope bitrate ranges for an ingest video allows for adjustments in transcoding targets, resulting in bitrate savings while maintaining visually negligible quality degradations. This optimization not only benefits service providers but also ensures that users receive high-quality video content tailored to their internet speeds and devices.
Experimental Results and Insights
The experimental results presented in this research affirm the effectiveness of the parametric R-D transcoding model. Quality improvements of up to 2 dB and bitrate savings of up to 46% of the original target bitrate are achievable by using this model in the transcoding process. These findings highlight the potential impact of the model on the efficiency and quality of video streaming services. As video streaming continues to grow in popularity, optimizing transcoding techniques becomes increasingly important in meeting the expectations of users with diverse internet capabilities and consuming devices.
Future Directions
Building upon this research, future directions could involve exploring the application of the parametric R-D transcoding model in emerging technologies such as virtual and augmented reality. As these technologies advance, the demand for high-quality video content will continue to rise. By integrating the model into the transcoding pipeline of virtual and augmented reality systems, developers can ensure that the immersive experience is augmented by visually compelling video content. Additionally, further research could focus on refining the model to account for specific characteristics and requirements of different video streaming applications, thus enabling even more accurate rate-distortion prediction.
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by jsendak | Apr 16, 2024 | Computer Science
Expert Commentary: Improving IPSEC with Multi-WAN, VPN, and 802.3ad
The complexity and scale of modern networks have necessitated the development of robust security mechanisms, and IPSEC has been at the forefront of this effort. However, the evolving landscape of the internet and the changing behavior of users worldwide have posed significant challenges to the effectiveness of IPSEC. As outlined in the article, the IEEE 802.3ad standard, which is commonly used in IPSEC models, has certain predictable aspects that can lead to potential design flaws, compromising the security of workstations.
To address these concerns and enhance the security of IPSEC, the article proposes leveraging the benefits of multiple ISPs (multi-WAN) and a link aggregation model, combined with the integration of an aspect of randomization in the network. This approach aims to introduce a sense of true randomness, making it more difficult for attackers to exploit any potential vulnerabilities in the network.
The proof of concept presented in the article, using the simulation of a double pendulum, demonstrates the potential of this approach. By designing a network topology that utilizes multiple WAN connections, incorporates 802.3ad link aggregation, and considers environmental factors such as transmission speed and the locations of WANs and VPNs, a sense of randomness can be achieved.
The key insight here is that randomness introduces an additional layer of complexity and unpredictability to the network, making it more challenging for attackers to identify patterns and exploit vulnerabilities. By distributing network traffic across multiple WAN connections and utilizing link aggregation, the network can effectively handle a larger data stream and mitigate the impact of potential failures or attacks on a single connection.
Moreover, the use of VPNs further enhances the security of the network by encrypting the data transmitted over the WAN connections. Combining VPNs with multi-WAN and link aggregation provides a comprehensive approach to improving IPSEC, ensuring the confidentiality and integrity of data transmitted within the network.
While the proof of concept described in the article shows promising results, it is important to consider the practical implementation challenges that may arise. Network administrators would need to carefully design and configure the network topology, taking into account factors such as load balancing, failover mechanisms, and the compatibility of network equipment with 802.3ad.
In conclusion, the approach outlined in this article, utilizing multi-WAN, VPN, and 802.3ad link aggregation, offers a compelling model for improving IPSEC. By introducing a sense of randomness through the utilization of multiple WAN connections and link aggregation, the network can enhance its security and resilience against potential attacks. This approach, when properly implemented and configured, has the potential to address the evolving challenges in maintaining a secure network infrastructure in today’s internet landscape.
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by jsendak | Apr 15, 2024 | Computer Science
arXiv:2404.08264v1 Announce Type: new
Abstract: Observations with distributed sensors are essential in analyzing a series of human and machine activities (referred to as ‘events’ in this paper) in complex and extensive real-world environments. This is because the information obtained from a single sensor is often missing or fragmented in such an environment; observations from multiple locations and modalities should be integrated to analyze events comprehensively. However, a learning method has yet to be established to extract joint representations that effectively combine such distributed observations. Therefore, we propose Guided Masked sELf-Distillation modeling (Guided-MELD) for inter-sensor relationship modeling. The basic idea of Guided-MELD is to learn to supplement the information from the masked sensor with information from other sensors needed to detect the event. Guided-MELD is expected to enable the system to effectively distill the fragmented or redundant target event information obtained by the sensors without being overly dependent on any specific sensors. To validate the effectiveness of the proposed method in novel tasks of distributed multimedia sensor event analysis, we recorded two new datasets that fit the problem setting: MM-Store and MM-Office. These datasets consist of human activities in a convenience store and an office, recorded using distributed cameras and microphones. Experimental results on these datasets show that the proposed Guided-MELD improves event tagging and detection performance and outperforms conventional inter-sensor relationship modeling methods. Furthermore, the proposed method performed robustly even when sensors were reduced.
The content of this article discusses the importance of distributed sensors in analyzing events in complex real-world environments. It points out that relying on information from a single sensor is often insufficient, and suggests that observations from multiple sensors should be integrated to comprehensively analyze events. This is where the concept of Guided-MELD comes in as a learning method to effectively combine distributed observations.
Guided-MELD stands for Guided Masked sELf-Distillation modeling, a technique that aims to supplement the information from a masked sensor with information from other sensors in order to detect events. By distilling the fragmented or redundant target event information obtained by the sensors, Guided-MELD enables the system to effectively analyze events without being overly dependent on any specific sensor.
In terms of the wider field of multimedia information systems, the concept of distributed sensors and integrating data from multiple locations and modalities is crucial. This approach allows for a more comprehensive analysis of events, especially in complex and extensive real-world environments. The use of Guided-MELD adds another layer to this analysis by providing a method to effectively distill and combine information from different sensors.
Moreover, the article highlights the importance of multi-disciplinary concepts such as Animations, Artificial Reality, Augmented Reality, and Virtual Realities in the context of event analysis. These technologies and techniques can contribute to enhancing the capabilities of distributed sensors and improving the accuracy of event tagging and detection.
To validate the effectiveness of the proposed method, the authors recorded two new datasets: MM-Store and MM-Office. These datasets consist of human activities in a convenience store and an office, recorded using distributed cameras and microphones. The experimental results on these datasets demonstrate that Guided-MELD improves event tagging and detection performance and outperforms conventional inter-sensor relationship modeling methods.
Overall, the concept of Guided-MELD and its application to the analysis of distributed multimedia sensor events provides valuable insights and practical implications. It showcases the importance of using multiple sensors and integrating data from various sources in order to effectively analyze events in complex real-world environments.
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by jsendak | Apr 15, 2024 | Computer Science
Expert Commentary: Accelerating Training of Large-scale Deep Learning Models
The article highlights the increasing demand for computing power and the associated energy costs and carbon emissions when training large-scale deep learning models such as BERT, GPT, and ViT. These models have revolutionized various domains, including natural language processing (NLP) and computer vision (CV). However, the computational requirements for training these models are exponentially growing, making it imperative to develop efficient training solutions.
The authors propose a multi-level framework for training acceleration, based on key observations of inter- and intra-layer similarities among feature maps and attentions. The framework utilizes three basic operators: Coalescing, De-coalescing, and Interpolation, which can be combined to build a V-cycle training process. This process progressively down- and up-scales the model size and transfers parameters between adjacent levels through coalescing and de-coalescing. The goal is to leverage a smaller, quickly trainable model to provide high-quality intermediate solutions for the next level’s larger network.
An important aspect of the framework is the interpolation operator, which is designed to overcome the symmetry of neurons caused by de-coalescing. This helps improve convergence performance. The experiments conducted on transformer-based language models such as BERT, GPT, and a vision model called DeiT demonstrate the effectiveness of the proposed framework. It achieves a reduction in computational cost by approximately 20% for training BERT/GPT-Base models and up to 51.6% for training the BERT-Large model, while maintaining performance.
This research addresses a crucial challenge in the field of deep learning, namely the high computational requirements for training large-scale models. By leveraging the inherent similarities within feature maps and attentions, the proposed framework significantly reduces training costs without sacrificing model performance. This has profound implications for both researchers and practitioners, as it allows for faster experimentation and deployment of state-of-the-art models, ultimately accelerating the pace of innovation in NLP, CV, and other domains.
Furthermore, the framework presents an interesting approach to managing computational resources in deep learning. By utilizing multi-level training and parameter transfer, it maximizes the efficiency of training processes. This aligns with the growing need for sustainable and energy-efficient AI systems, as reducing energy consumption and carbon emissions is critical for mitigating the environmental impact of deep learning.
In terms of future developments, it would be valuable to explore the applicability of the proposed framework to other types of deep learning models and domains. Additionally, investigating the potential for further reducing computational costs while maintaining or even improving performance would be an exciting avenue of research. As deep learning models continue to grow in size and complexity, finding efficient training strategies will remain a crucial area of investigation.
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